Intelligent sea transport document urging system and method

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent sea transport document urging system and method, and aims to adjust storage resources according to data access frequency, analyze task relevance and processing capacity in an urging path, optimize the path, evaluate task execution state, progress and delay risk, monitor data flow change and identify risk nodes. And adjusting node paths and task priorities, and planning a task scheduling sequence. According to the invention, by dynamically adjusting the data storage resources, the high-frequency data is ensured to be preferentially stored in the rapid equipment, and the storage delay and the resource waste are effectively reduced. And the real-time optimization of the urging path is intelligently scheduled according to the task priority and the node processing capability, so that task delay and backlog are avoided. Through data flow monitoring and fluctuation analysis, risk nodes are identified in time, path adjustment is carried out, and smooth execution of tasks is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent shipping document urging system and method. Background Art

[0002] The field of data processing technology includes the collection, storage, processing and analysis of various types of data. The core content includes data acquisition and transmission, data storage and management, data processing and analysis methods, and data visualization. With the development of information technology, data processing technology has been widely used in various industries, especially in logistics, finance, medical and other fields, playing an important role in data processing and management. It mainly involves how to process large amounts of complex data through reasonable technical means, thereby improving system efficiency, reducing human intervention, and improving the level of automation, thereby optimizing decision support and business processes.

[0003] Among them, the intelligent shipping document reminder system refers to a system that uses information technology to optimize the problems existing in the document reminder work in shipping logistics. It mainly includes data management and transmission in the process of shipping document reminders, and uses automated tools to improve the efficiency of reminders. Specifically, by collecting shipping document information and connecting with relevant systems to achieve automatic comparison and tracking of document information, reminder notifications are sent based on set rules to ensure accurate information transmission and timely completion of reminder work. By using data storage and management technology, it supports multi-party data sharing and real-time updates, ensures the efficiency and timeliness of the reminder process, and thus improves the overall management level of shipping logistics.

[0004] In the prior art, the management of storage resources is mostly static, and it is impossible to make real-time adjustments based on changes in data types, access frequencies, and node loads, resulting in unreasonable storage of high-frequency data, and access speeds and storage efficiency cannot be guaranteed. The execution of the order collection path also lacks flexible adjustments based on task priorities and progress, which can easily cause task progress to lag behind, and cannot respond to actual demand changes in a timely manner, resulting in low efficiency in order collection. In addition, the existing system has weak monitoring and risk prevention capabilities for traffic changes, and cannot promptly identify and respond to traffic fluctuations or excessive node loads, thereby affecting the stability of the system and the smooth completion of task execution. These deficiencies limit the flexibility and efficiency of the system in handling complex tasks, especially when faced with large-scale data or changing environments, and cannot effectively avoid delays and waste of resources, affecting the overall management effect of shipping document collection. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent shipping document reminder system and method.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an intelligent shipping document reminder system, the system comprising:

[0007] The data storage management module obtains the type, storage requirements and access frequency of shipping document data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, and generates data storage allocation results;

[0008] The order urging path optimization module calls the data access frequency and storage capacity of each storage node in the data storage allocation result, analyzes the task correlation and processing capacity between each node in the order urging path, and generates an optimized order urging path;

[0009] The dynamic scheduling module extracts the task execution status, progress information and delay risk of each node in the optimized order urging path, compares the task processing capacity with the current processing progress, and generates the order urging path execution result;

[0010] The data flow monitoring module monitors the data flow information of the execution path nodes based on the execution results of the order reminder path, analyzes the change trend of the node data flow, and reallocates the execution path of the risk node to generate the flow fluctuation monitoring results;

[0011] The feedback adjustment module analyzes the flow fluctuation amplitude and path load of the current path node based on the flow fluctuation monitoring results, dynamically adjusts the task execution priority of the path node, plans the task scheduling sequence according to the priority, and generates an adjusted order urging task report.

[0012] As a further scheme of the present invention, the data storage allocation results include high-frequency data storage nodes, low-frequency data storage nodes, storage capacity allocation, and node load distribution; the optimized order collection path includes task priority adjustment, task processing capability matching, correlation analysis between path nodes, and storage capacity and processing capability matching evaluation; the order collection path execution results include task execution status, task progress, delay risk, ship navigation status, and port scheduling status; the traffic fluctuation monitoring results include data traffic change trends, risk node traffic fluctuations, nodes with excessive traffic fluctuation amplitudes, and node path adjustment suggestions; the adjusted order collection task report includes task priority adjustment results, task progress evaluation, node processing capability evaluation, node delay conditions, and task scheduling sequence optimization.

[0013] As a further solution of the present invention, the data storage management module includes:

[0014] The data classification submodule obtains the data types and storage requirements of shipping documents, analyzes and compares the access frequency of each type of data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, and generates data classification results;

[0015] The data storage allocation submodule evaluates the load and storage capacity of each storage node based on the data classification results, and adopts the formula based on the data storage requirements:

[0016] ;

[0017] The storage allocation result is obtained by calculation, and the preliminary storage allocation result is generated by combining the node load, storage capacity and data access frequency differences;

[0018] in, Represents the storage capacity of the SSD node, Represents the storage capacity of the HDD node, and Represent the access frequency of high-frequency and low-frequency data respectively, and Represent the load status of SSD nodes and HDD nodes respectively. Represents storage requirements, Represents the storage allocation result;

[0019] The storage allocation adjustment submodule monitors the real-time changes of the storage node load according to the preliminary storage allocation result, analyzes the difference between the current storage capacity and the demand, performs dynamic adjustment of storage allocation, and generates data storage allocation results.

[0020] As a further solution of the present invention, the order urging path optimization module includes:

[0021] The data access analysis submodule calls the data access frequency and storage capacity of each storage node according to the data storage allocation result, analyzes the node task relevance and data processing requirements in combination with the order urging task priority and progress, calculates and obtains the data access matching degree of each node, and generates the data access matching degree;

[0022] The node processing capacity evaluation submodule evaluates the processing capacity of each storage node based on the priority and progress of the order urging task, analyzes the storage capacity and load status of the node, and calculates the matching degree between the node processing capacity and the task requirements. The formula is:

[0023] ;

[0024] The calculation obtains the matching degree between the node processing capacity and the task requirements, and generates the node processing capacity evaluation result by combining the storage capacity, load status and task requirements;

[0025] in, Represents the degree of match between the node processing capability and the task requirements. Represents the storage capacity of the node, Represents the load rate of the node, represents the processing time requirement of the task, Represents the priority of the task;

[0026] The order reminder path optimization submodule optimizes the nodes in the order reminder path according to the data access matching degree and node processing capacity evaluation results, analyzes the task correlation and processing capacity matching between nodes, adjusts the storage resources and task allocation of the nodes in the path, and generates an optimized order reminder path.

[0027] As a further solution of the present invention, the dynamic scheduling module includes:

[0028] The task status extraction submodule extracts the task execution status, progress information and delay risk of each node in the optimized order reminder path, obtains the current execution progress of each task, monitors the progress of the node task, and obtains the task execution processing status;

[0029] The processing capacity and progress comparison submodule compares the processing capacity of the task with the current progress according to the task execution processing status, evaluates whether each node can complete the task on time, and calculates the gap between the processing capacity and progress of the node using the formula:

[0030] ;

[0031] The calculation obtains the processing capacity and progress gap of each node and generates the task progress matching result;

[0032] in, Represents the gap between processing capacity and progress, Represents the storage capacity of the node, Represents the load rate of the node, represents the processing time requirement of the task, Represents the current progress of the task. Represents the total progress of the task;

[0033] The path adjustment and execution submodule adjusts the task path of each node in real time according to the task progress matching results and the ship's navigation status and port scheduling status during the maritime document reminder process. It replans and executes the task path based on the processing capacity and task progress of each node to generate the reminder path execution result.

[0034] As a further solution of the present invention, the data flow monitoring module includes:

[0035] The data collection submodule monitors the data flow information of each node in the execution result of the order urging path in different time periods, collects flow information in real time based on task processing capability and delay risk assessment, and generates real-time data flow information;

[0036] The data analysis submodule analyzes the data flow change trend of each node according to the real-time data flow information, using the formula:

[0037] ;

[0038] Calculate and obtain the traffic fluctuation percentage, compare it with the preset fluctuation threshold, filter out the risk nodes whose fluctuation exceeds the threshold, and generate a traffic fluctuation trend analysis report;

[0039] in, Represents the flow fluctuation percentage, Represents the node data traffic at the current moment, Represents the node data traffic at the previous moment, represents the average processing delay of the node, Represents the total amount of tasks, Represents the length of the time period;

[0040] The path adjustment submodule replans the execution path of the risk node according to the traffic fluctuation trend analysis report, analyzes the traffic fluctuation of each risk node, optimizes the execution order between nodes, and generates traffic fluctuation monitoring results.

[0041] As a further solution of the present invention, the feedback adjustment module includes:

[0042] The flow monitoring submodule detects the flow fluctuation amplitude and trend of the current path node according to the flow fluctuation monitoring result, obtains the flow difference and fluctuation rate of each path node, and evaluates the flow fluctuation amplitude of the path node to generate a path flow evaluation result;

[0043] The path load analysis submodule analyzes the load of the path nodes based on the path flow evaluation results, calculates the load index of each node, compares the relationship between the load and the flow fluctuation amplitude, and selects the path nodes with heavier loads to obtain the load ratio of the path nodes using the formula:

[0044] ;

[0045] Obtain the load index of each node through calculation and generate path node load data;

[0046] in, For the The load index of the nodes, For the The traffic fluctuation amplitude of each node, is the node traffic, is the average flow rate, is the total number of nodes;

[0047] The task scheduling optimization submodule analyzes the task execution priority of the current path node according to the path node load data and path traffic evaluation results, calculates the adjustment requirements of the task scheduling sequence, adjusts the node task scheduling sequence, determines whether the task needs to be re-planned, and generates an adjusted order urging task report.

[0048] An intelligent method for urging shipping documents, the intelligent method for urging shipping documents being executed based on the intelligent shipping document urging system, comprising the following steps:

[0049] S1: Obtain the data type, storage requirements and access frequency of shipping documents, divide the data into high-frequency data and low-frequency data according to the access frequency, store the high-frequency data in SSD nodes and the low-frequency data in HDD nodes, monitor the node load and storage capacity in real time, dynamically adjust the data storage allocation according to the current load situation, and generate the storage allocation result;

[0050] S2: calling the data access frequency and storage capacity in the storage allocation result, analyzing the task relevance and processing capacity of each node, combining the priority and progress of the order urging task, evaluating the matching degree of the storage and processing capacity of the nodes in the path, and generating an optimized order urging path;

[0051] S3: According to the task execution status and progress information of each node in the optimized order urging path, combined with the ship's navigation status and port scheduling status, the task's processing capacity and current progress are evaluated in real time, the delay risk is determined, and the task execution path is adjusted based on the evaluation result to generate the order urging path execution result;

[0052] S4: Based on the execution result of the order urging path, monitor the real-time data flow of the path nodes, analyze the data flow change trend, screen the risk nodes whose flow fluctuation exceeds the fluctuation threshold, readjust the execution path of the risk nodes, and generate the flow fluctuation monitoring result;

[0053] S5: Based on the traffic fluctuation monitoring results, analyze the traffic fluctuation amplitude and path load of the current path node, dynamically adjust the path node task execution priority, evaluate the task progress, node processing capacity and delay of each node, determine whether the task scheduling order needs to be adjusted, and generate an adjusted order urging task report.

[0054] Compared with the prior art, the advantages and positive effects of the present invention are:

[0055] In the present invention, by dynamically adjusting data storage resources, high-frequency data is ensured to be stored in fast devices first, effectively reducing storage delays and resource waste. Real-time optimization of the order-collecting path is intelligently scheduled according to task priority and node processing capacity, avoiding task delays and backlogs. Through data flow monitoring and fluctuation analysis, risk nodes are identified in a timely manner and path adjustments are made, effectively ensuring the smooth execution of tasks. The overall process is automated and intelligent, improving system efficiency and adaptability, and making order-collecting work more efficient and stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a system flow chart of the present invention;

[0057] Figure 2 Obtain a flow chart for the data storage management module of the present invention;

[0058] Figure 3 Obtain a flow chart for the order urging path optimization module of the present invention;

[0059] Figure 4 Obtaining a flow chart for the dynamic scheduling module of the present invention;

[0060] Figure 5 Obtaining a flow chart for the data flow monitoring module of the present invention;

[0061] Figure 6 A flow chart is obtained for the feedback adjustment module of the present invention. DETAILED DESCRIPTION

[0062] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0063] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0064] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0065] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0066] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0067] See also Figure 1 The present invention provides a technical solution: an intelligent shipping document reminder system, the system comprising:

[0068] The data storage management module obtains the type, storage requirements and access frequency of shipping document data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, stores high-frequency data in SSD nodes and low-frequency data in HDD nodes, adjusts storage allocation in real time according to the load and storage capacity of each node, and generates data storage allocation results;

[0069] The order urging path optimization module calls the data access frequency and storage capacity of each storage node in the data storage allocation result, refers to the priority and progress of the order urging task, analyzes the task correlation and processing capacity between each node in the order urging path, evaluates the matching degree between the storage capacity and processing capacity in the order urging path, and generates an optimized order urging path;

[0070] The dynamic scheduling module extracts the task execution status, progress information and delay risk of each node in the optimized order reminder path, compares the task processing capacity with the current processing progress, and adjusts the task path in real time based on the ship's navigation status and port scheduling status during the ocean document reminder process to generate the order reminder path execution result;

[0071] The data flow monitoring module monitors the data flow information of the execution path nodes based on the execution results of the order reminder path, analyzes the change trend of the node data flow, screens the risk nodes whose flow fluctuation exceeds the fluctuation threshold, reallocates the execution path of the risk nodes, and generates flow fluctuation monitoring results;

[0072] Based on the traffic fluctuation monitoring results, the feedback adjustment module analyzes the traffic fluctuation amplitude and path load of the current path node, dynamically adjusts the task execution priority of the path node, evaluates the task progress, node processing capacity, and delay situation of each node after adjustment, determines whether to plan the task scheduling sequence, and generates an adjusted order reminder task report.

[0073] The data storage allocation results include high-frequency data storage nodes, low-frequency data storage nodes, storage capacity allocation, and node load distribution; the optimized order collection path includes task priority adjustment, task processing capacity matching, correlation analysis between path nodes, and storage capacity and processing capacity matching evaluation; the order collection path execution results include task execution status, task progress, delay risk, ship navigation status, and port scheduling status; the traffic fluctuation monitoring results include data traffic change trends, risk node traffic fluctuations, nodes with excessive traffic fluctuations, and node path adjustment suggestions; the adjusted order collection task report includes task priority adjustment results, task progress evaluation, node processing capacity evaluation, node delays, and task scheduling sequence optimization.

[0074] See also Figure 2 , the data storage management module includes:

[0075] The data classification submodule obtains the data types and storage requirements of shipping documents, analyzes and compares the access frequency of each type of data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, and generates data classification results;

[0076] According to the data type and storage requirements of the acquired shipping documents, combined with the access frequency of each type of data, detailed classification processing is performed. Assume that there are two data types: type A and type B. Type A data is frequently accessed and belongs to high-frequency data, while type B data is less frequently accessed and belongs to low-frequency data. In actual applications, data type A may be real-time tracking cargo information that is frequently accessed, while data type B may be historical records that are only occasionally queried. After quantitatively analyzing the access frequency of each type of data, high-frequency data is classified into SSD nodes for storage, and low-frequency data is stored in HDD nodes. In actual operation, when obtaining the access frequency, the number of accesses to each data point can be recorded by analyzing the log, the access frequency of each data point can be calculated, and it can be compared with the storage capacity and reading performance of the SSD and HDD nodes, and finally the optimal match between data classification and storage can be achieved.

[0077] The data storage allocation submodule evaluates the load and storage capacity of each storage node based on the data classification results, and uses the formula based on the data storage requirements:

[0078] ;

[0079] The storage allocation result is obtained by calculation, and the preliminary storage allocation result is generated by combining the node load, storage capacity and data access frequency differences;

[0080] in, Represents the storage capacity of the SSD node, Represents the storage capacity of the HDD node, and Represent the access frequency of high-frequency and low-frequency data respectively, and Represent the load status of SSD nodes and HDD nodes respectively. Represents storage requirements, Represents the storage allocation result;

[0081] Assumption: SSD node storage capacity ( ): 500GB, HDD node storage capacity ( ): 2TB (2000GB), data storage requirements ( ): 1.4TB (1400GB), high data access frequency ( ): 80 times / hour, low-frequency data access frequency ( ): 10 times / hour, SSD node load ( ): 75%, HDD node load ( ): 60%;

[0082] Calculation process:

[0083] Calculate the access frequency difference between high-frequency and low-frequency data:

[0084] ;

[0085] Calculate the first term of the storage allocation formula:

[0086] ;

[0087] Calculate the load difference term:

[0088] ;

[0089] ;

[0090] Calculate the storage allocation results:

[0091] ;

[0092] Analysis of calculation results:

[0093] The final storage allocation result ,The results show that the adjustment index of storage ,allocation is high, which indicates that the current storage allocation ,scheme has large load differences or storage capacity imbalance, and needs to ,be optimized.

[0094] The storage allocation adjustment submodule monitors the real-time changes in the storage node load based on the preliminary storage allocation results, analyzes the difference between the current storage capacity and demand, performs dynamic storage allocation adjustments, and generates data storage allocation results;

[0095] Continuously monitor the storage capacity and load status of each node, and perform optimization adjustments to storage allocation through real-time data updates. For example, assume that the current storage utilization rate of the SSD node is 85%, while the storage utilization rate of the HDD node is 60%. At this point, it can be found that the load of the SSD node is too high, which may affect the performance of the system. Therefore, dynamic adjustments are performed to migrate part of the high-frequency data (such as type A data) from the SSD to the HDD node to balance the load. In this process, by real-time monitoring the storage capacity and load status of the system, data is reallocated according to dynamic changes to ensure the smooth operation of the entire storage environment, and finally generate an adjusted data storage allocation plan. This process dynamically adjusts the distribution of data between SSD and HDD nodes by calculating the storage capacity, load, and data access frequency of each node, ultimately ensuring that the load of each node is maintained within a reasonable range.

[0096] See also Figure 3 , the order reminder path optimization module includes:

[0097] The data access analysis submodule calls the data access frequency and storage capacity of each storage node according to the data storage allocation result, analyzes the node task relevance and data processing requirements in combination with the order urging task priority and progress, calculates and obtains the data access matching degree of each node, and generates the data access matching degree;

[0098] Assume that there are multiple tasks, among which task A has a higher priority and task B has a lower priority. Each task is processed in a different storage node, and the node has different load and storage capacity. Therefore, it is necessary to evaluate each node to determine whether it can efficiently process the task. In this task, we first need to obtain the storage capacity and load rate of each node. Take node X as an example, its storage capacity is 500GB and the current load rate is 60%; node Y has a storage capacity of 1TB and a load rate of 40%. Based on these data, calculate the ability of each node to process tasks. The load rate of the node has a direct impact on its processing capacity. A high-load node may slow down when processing tasks, which in turn affects the speed of overall task completion. Therefore, it is key to analyze the degree of match between the node's processing capacity and task requirements, and generate the node processing capacity and task matching degree for subsequent path optimization and task allocation.

[0099] The node processing capacity evaluation submodule evaluates the processing capacity of each storage node based on the priority and progress of the order urging task, analyzes the storage capacity and load status of the node, and calculates the matching degree between the node processing capacity and the task requirements. The formula is:

[0100] ;

[0101] The calculation obtains the matching degree between the node processing capacity and the task requirements, and generates the node processing capacity evaluation result by combining the storage capacity, load status and task requirements;

[0102] in, Represents the degree of match between the node processing capability and the task requirements. Represents the storage capacity of the node, Represents the load rate of the node, represents the processing time requirement of the task, Represents the priority of the task;

[0103] Assume there are two nodes and , and two tasks and , the data is as follows:

[0104] Node X: Storage capacity , load factor ;

[0105] Node Y: Storage capacity , load factor ;

[0106] The processing time requirements and priorities of Task A and Task B are as follows:

[0107] Task A: Processing time requirements , priority ;

[0108] Task B: Processing time requirements , priority ;

[0109] Calculate the matching degree of node X:

[0110] Substituting the known values ​​into the formula:

[0111] ;

[0112] Calculate each part:

[0113] ;

[0114] ;

[0115] ;

[0116] Substituting these calculated values:

[0117] ;

[0118] The results show that node X is able to process task A, and the matching value is 7.61, which means that node X can complete the processing of task A under high load, but the efficiency is relatively low. Therefore, when allocating tasks, it may be necessary to give priority to allocating tasks to nodes with lower loads to improve the overall processing efficiency.

[0119] Calculate the matching degree of node Y:

[0120] Substituting the known values ​​into the formula:

[0121] ;

[0122] Calculate each part: ;

[0123] ;

[0124] ;

[0125] Substituting these calculated values:

[0126] ;

[0127] The results show that node Y can efficiently process task B, and the matching value is 12, which means that node Y can process task B more quickly when the load is low and the storage capacity is large. Compared with node X, node Y has stronger processing capability, so task B should be assigned to node Y first.

[0128] The order urging path optimization submodule optimizes the nodes in the order urging path according to the data access matching degree and node processing capacity evaluation results, analyzes the task relevance and processing capacity matching between nodes, adjusts the storage resources and task allocation of the nodes in the path, and generates an optimized order urging path;

[0129] Assume that the order urging path involves multiple nodes, where nodes X and Y process tasks A and B. Node X has weak processing power. Although its storage capacity is small, its load is high. Node Y has strong processing power and can efficiently process tasks. Based on the node processing capacity evaluation results, tasks A and B need to be reallocated. In this process, task A is preferentially assigned to node Y, while task B is assigned to node X. In this way, it can ensure that tasks can be efficiently processed according to priority and node capacity. The optimized path not only improves the efficiency of task completion, but also reduces the burden on high-load nodes. A more efficient order urging path can be obtained through the optimized path.

[0130] See also Figure 4 , the dynamic scheduling module includes:

[0131] The task status extraction submodule extracts the task execution status, progress information and delay risk of each node in the optimized order reminder path, obtains the current execution progress of each task, monitors the progress of node tasks, and obtains the task execution processing status;

[0132] Assuming that the task progress of node A is 70% and the task progress of node B is 40%, there is a certain lag in ship scheduling, which will have a certain impact on the task progress. Therefore, it is necessary to monitor the node task progress, delay risk and scheduling status in real time to ensure the smooth completion of tasks between nodes. In practice, by monitoring the scheduling status of the port and the navigation status of the ship, the execution data of each node can be collected in real time, the delay risk of the task can be evaluated, and data support can be provided for subsequent optimization. The task execution status and delay risk assessment can provide a reference for subsequent task path optimization.

[0133] The processing capacity and progress comparison submodule compares the processing capacity of the task with the current progress according to the task execution processing status, evaluates whether each node can complete the task on time, and calculates the gap between the processing capacity and progress of the node using the formula:

[0134] ;

[0135] The calculation obtains the processing capacity and progress gap of each node and generates the task progress matching result;

[0136] in, Represents the gap between processing capacity and progress, Represents the storage capacity of the node, Represents the load rate of the node, represents the processing time requirement of the task, Represents the current progress of the task. Represents the total progress of the task;

[0137] Assume that the task processing capacity of node A is 300 documents / hour, the processing capacity of task B is 200 documents / hour, the current progress of task A is 70%, the progress of task B is 40%, the total progress of task A and task B is The progress information of the task is compared with the processing capacity of the node, and the remaining processing time of each node is calculated according to the formula in the processing capacity and progress comparison submodule.

[0138] Calculate the remaining amount of task A:

[0139] ;

[0140] Node A remaining time:

[0141] ;

[0142] Substitute into the formula: Assume that the load rate of node A is , the processing time requirement of task A :

[0143] ;

[0144] Calculate each part:

[0145] , , ;

[0146] Substituting the values ​​into the formula:

[0147] ;

[0148] The results show that there is a certain gap between the processing capacity of node A and the progress of task A. The progress matching degree is 0.68, which means that although node A can process task A, due to its high load, the completion time of the remaining tasks may be longer. This gap indicates that the processing efficiency of node A is low and appropriate optimization or adjustment of task allocation is needed.

[0149] Calculate the remaining amount of task B:

[0150] ;

[0151] Node B remaining time:

[0152] ;

[0153] Substitute into the formula: Assume that the load rate of node B is , the processing time requirement of task B :

[0154] ;

[0155] Calculate each part:

[0156] , , ;

[0157] Substituting the values ​​into the formula:

[0158] ;

[0159] The results show that the processing capability of node B is highly matched with the progress of task B, with a progress matching degree of 1.19, indicating that the processing capability of node B is well matched with the progress of task B, and the task can be completed efficiently. This result shows that node B can complete task B quickly and the progress gap is small.

[0160] The path adjustment and execution submodule adjusts the task path of each node in real time according to the task progress matching results, combined with the ship's navigation status and port scheduling status during the maritime document urging process, and replans and executes the task path according to the processing capacity and task progress of each node to generate the urging path execution results;

[0161] Assuming that the current navigation status of the ship will cause the processing progress of task A to lag behind, and the processing capacity of the port is limited, it is necessary to consider how to readjust the task path. Specifically, if the task progress of node A lags behind, the remaining documents can be assigned to node B, or the task processing order of node A can be adjusted to reduce the risk of task delays. For example, if node A is expected to be unable to process the remaining tasks within 1 hour, part of the tasks can be assigned to node B in advance, or the processing order can be adjusted when the ship approaches the port. This is implemented by real-time monitoring of factors such as the ship's navigation status and port scheduling status, and ultimately generates an optimized order execution path execution result.

[0162] See also Figure 5 , the data flow monitoring module includes:

[0163] The data collection submodule monitors the data flow information of each node in the execution results of the order reminder path in different time periods, collects flow information in real time based on task processing capacity and delay risk assessment, and generates real-time data flow information;

[0164] Suppose we have two nodes, node A and node B, the task processing capacity of node A is 300 documents / hour, and node B is 250 documents / hour. The system needs to monitor and record the data flow of nodes A and B in real time. Taking node A as an example, assuming that its current task progress is 70%, and the task progress of node B is 40%, at this time, it is necessary to monitor the data flow of each node according to the real-time data flow of the node. For example, the real-time data flow of node A is 300 documents / hour, and the real-time data flow of node B is 250 documents / hour. At this time, the system will collect the flow data of each node in real time, including the percentage of node task completion, the total amount of tasks, etc., and generate the real-time data flow information of each node as the data basis for subsequent analysis. Based on these real-time collected data, the system can analyze the task flow trend of each node to help further determine whether the node task is proceeding according to the scheduled progress.

[0165] The data analysis submodule analyzes the data flow change trend of each node based on the real-time data flow information, using the formula:

[0166] ;

[0167] Calculate and obtain the traffic fluctuation percentage, compare it with the preset fluctuation threshold, filter out the risk nodes whose fluctuation exceeds the threshold, and generate a traffic fluctuation trend analysis report;

[0168] in, Represents the flow fluctuation percentage, Represents the node data traffic at the current moment, Represents the node data traffic at the previous moment, represents the average processing delay of the node, Represents the total amount of tasks, Represents the length of the time period;

[0169] According to the monitored node data traffic, combined with the preset traffic fluctuation threshold, the traffic change trend of each node is analyzed to screen out risk nodes whose traffic fluctuation exceeds the threshold. Assuming that the traffic of node A suddenly jumps from 300 documents / hour to 600 documents / hour, and this fluctuation continues to exceed the set threshold by 10% (for example, the threshold is 30 documents / hour), then node A will be marked as a risk node, and the traffic fluctuation percentage of node A will be calculated;

[0170] We will use actual data for calculation. Assume that the traffic of node A is 300 documents / hour ( ) jumped to 600 documents / hour ( ), the average processing delay of the node is The total amount of tasks is , the time period is ,So: , , , ;

[0171] Calculate the sum of the absolute differences in flow changes:

[0172] ;

[0173] Calculate the sum of squared flows:

[0174] ;

[0175] Calculate the delay adjustment factor:

[0176] ;

[0177] Substitute into the formula to calculate the flow fluctuation percentage:

[0178] ;

[0179] According to the calculation results, the traffic fluctuation percentage of node A is 0.4471 or 44.71%, indicating that the traffic fluctuation of node A is very large, exceeding the preset fluctuation threshold. It needs to be marked as a risk node and the path needs to be adjusted;

[0180] The results show that the traffic fluctuation percentage of node A is much higher than the set threshold, indicating that the traffic change of node A is too drastic and may bring instability to task execution. Therefore, node A will be marked as a risky node and its task allocation and execution path will be re-evaluated.

[0181] The path adjustment submodule replans the execution path of the risk node based on the traffic fluctuation trend analysis report, analyzes the traffic fluctuation of each risk node, optimizes the execution order between nodes, and generates traffic fluctuation monitoring results;

[0182] After screening out risky nodes, the path adjustment submodule will adjust the execution path of nodes with large data traffic fluctuations based on the processing capacity, latency and total number of tasks of each node to avoid task delays caused by excessive node load. Specifically, when it is found that the traffic fluctuation of node A is abnormal and exceeds the set threshold, the system will transfer part of the task traffic to node B with a lighter load through task redistribution to ensure that the data traffic of each node remains within a stable range. For example, if the task volume of node A is 1,000 documents, and its traffic fluctuation exceeds the threshold, the system may transfer the task of 500 documents to node B, keep the load of node A within a reasonable range, and ensure that the task is not affected by real-time calculation of node B's processing capacity. After adjustment, the system generates traffic fluctuation monitoring results to ensure the smooth execution of the task path of the entire process.

[0183] See also Figure 6 , the feedback adjustment module includes:

[0184] The flow monitoring submodule detects the flow fluctuation amplitude and trend of the current path node according to the flow fluctuation monitoring results, obtains the flow difference and fluctuation rate of each path node, and evaluates the flow fluctuation amplitude of the path node to generate the path flow evaluation result;

[0185] Data is collected for each path node to record its flow fluctuations, especially the flow difference of each path node. The flow difference can be obtained by comparing the input and output flow of the node with the historical average flow. Specifically, assuming that the flow of path node A is 50 at a certain moment, and the historical average flow of the node is 40, the flow difference of the node is 50-40=10. Next, by comparing the fluctuation amplitude of each node, data analysis is performed to further quantify the flow fluctuation amplitude of each node, which can reflect the flow change trend of the current path node. The flow pressure of the node can be predicted through the flow fluctuation amplitude, providing a basis for subsequent path load analysis. In actual applications, if the flow fluctuation amplitude of node A is large, it means that the node has a high flow fluctuation, and it may be necessary to adjust the task execution strategy or allocate resources in priority to deal with the impact of potential flow fluctuations on task execution. The generated flow fluctuation amplitude value is the quantitative result of the flow fluctuation of the node.

[0186] The path load analysis submodule analyzes the load of path nodes based on the path flow evaluation results, calculates the load index of each node, compares the relationship between load and flow fluctuation amplitude, and selects the path nodes with heavier loads to obtain the load ratio of the path nodes using the formula:

[0187] ;

[0188] Obtain the load index of each node through calculation and generate path node load data;

[0189] in, For the The load index of the nodes, For the The traffic fluctuation amplitude of each node, is the node traffic, is the average flow rate, is the total number of nodes;

[0190] Assume that the flow fluctuation ranges of path nodes A, B, and C are 10, 8, and 12 respectively, and the processing capabilities of the nodes are 5, 4, and 6 respectively, and the total number of nodes is 3. First, the average flow rate needs to be calculated. The average flow fluctuation range is:

[0191] ; Calculate the load index for each node:

[0192] For node A, the traffic fluctuation range is 10 and the processing capacity is 5:

[0193] ;

[0194] For node B, the traffic fluctuation range is 8 and the processing capacity is 4:

[0195] ;

[0196] For node C, the traffic fluctuation range is 12 and the processing capacity is 6:

[0197] ;

[0198] The results show that node C has the highest load index, which means that the node is under heavy load and may need to prioritize tasks or optimize task scheduling. Node B has the lowest load index, which means that its load pressure is relatively light and its scheduling priority may be low. The calculation of the load index can help determine which nodes are under heavy load and need to optimize scheduling tasks. The resulting path node load index provides a quantitative basis for subsequent task scheduling.

[0199] The task scheduling optimization submodule analyzes the task execution priority of the current path node based on the path node load data and path traffic evaluation results, calculates the adjustment requirements of the task scheduling sequence, adjusts the node task scheduling sequence, determines whether the task needs to be re-planned, and generates an adjusted order urging task report;

[0200] Based on the load index and traffic fluctuation range, the priority of node task execution is calculated. For nodes A, B, and C, the relationship between their traffic fluctuation range and load index is first calculated, and the task priority of each node is obtained in combination with the task processing capacity of the node. Since nodes A, B, and C have the same load index but different traffic fluctuation ranges, the priority can be determined by the traffic fluctuation range. In practical applications, it is assumed that the traffic fluctuation range of node A is 10, the traffic fluctuation range of node B is 8, and the traffic fluctuation range of node C is 12. Based on these values, it can be determined that node C has the highest priority, followed by node A, and finally node B. According to this sorting method, the task scheduling order can be adjusted to give priority to nodes with larger traffic fluctuation ranges, thereby improving the efficiency of task execution. Finally, an adjusted order-urging task report is generated, and the report content covers the priority sorting and task adjustment plan of each node.

[0201] An intelligent method for urging shipping documents comprises the following steps:

[0202] S1: Obtain the data type, storage requirements and access frequency of shipping documents, divide the data into high-frequency data and low-frequency data according to the access frequency, store the high-frequency data in SSD nodes and the low-frequency data in HDD nodes, monitor the node load and storage capacity in real time, dynamically adjust the data storage allocation according to the current load situation, and generate the storage allocation result;

[0203] S2: Call the data access frequency and storage capacity in the storage allocation result, analyze the task relevance and processing capacity of each node, combine the priority and progress of the order urging task, evaluate the matching degree of the storage and processing capacity of the nodes in the path, and generate an optimized order urging path;

[0204] S3: Based on the task execution status and progress information of each node in the optimized order-urging path, combined with the ship's navigation status and port scheduling status, the task's processing capacity and current progress are evaluated in real time, the delay risk is determined, and the task execution path is adjusted based on the evaluation results to generate the order-urging path execution results;

[0205] S4: Based on the execution results of the order reminder path, monitor the real-time data traffic of the path nodes, analyze the data traffic change trend, screen the risk nodes whose traffic fluctuation exceeds the fluctuation threshold, readjust the execution path of the risk nodes, and generate traffic fluctuation monitoring results;

[0206] S5: Based on the traffic fluctuation monitoring results, analyze the traffic fluctuation amplitude and path load of the current path node, dynamically adjust the path node task execution priority, evaluate the task progress, node processing capacity and delay of each node, determine whether the task scheduling sequence needs to be adjusted, and generate the adjusted order urging task report.

[0207] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent shipping document reminder system, characterized in that: The system comprises: The data storage management module obtains the type, storage requirements and access frequency of shipping document data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, and generates data storage allocation results; The order urging path optimization module calls the data access frequency and storage capacity of each storage node in the data storage allocation result, analyzes the task correlation and processing capacity between each node in the order urging path, and generates an optimized order urging path; The dynamic scheduling module extracts the task execution status, progress information and delay risk of each node in the optimized order urging path, compares the task processing capacity with the current processing progress, and generates the order urging path execution result; The dynamic scheduling module includes: The task status extraction submodule extracts the task execution status, progress information and delay risk of each node in the optimized order reminder path, obtains the current execution progress of each task, monitors the progress of the node task, and obtains the task execution processing status; The processing capacity and progress comparison submodule compares the processing capacity of the task with the current progress according to the task execution processing status, evaluates whether each node can complete the task on time, and calculates the gap between the processing capacity and progress of the node using the formula: ； The calculation obtains the processing capacity and progress gap of each node and generates the task progress matching result; in, Represents the gap between processing capacity and progress, Represents the storage capacity of the node, Represents the load rate of the node, represents the processing time requirement of the task, Represents the current progress of the task. Represents the total progress of the task; The path adjustment and execution submodule adjusts the task path of each node in real time according to the task progress matching result and the ship navigation status and port scheduling status during the marine document urging process, replans the task path and executes it according to the processing capacity and task progress of each node, and generates the urging path execution result; The data flow monitoring module monitors the data flow information of the execution path nodes based on the execution results of the order reminder path, analyzes the change trend of the node data flow, and reallocates the execution path of the risk node to generate the flow fluctuation monitoring results; The feedback adjustment module analyzes the flow fluctuation amplitude and path load of the current path node based on the flow fluctuation monitoring results, dynamically adjusts the task execution priority of the path node, plans the task scheduling sequence according to the priority, and generates an adjusted order urging task report.

2. The intelligent shipping document reminder system according to claim 1 is characterized by: The data storage allocation results include high-frequency data storage nodes, low-frequency data storage nodes, storage capacity allocation, and node load allocation; the optimized order urging path includes task priority adjustment, task processing capacity matching, correlation analysis between path nodes, and storage capacity and processing capacity matching evaluation; The execution result of the order reminder path includes task execution status, task progress, delay risk, ship navigation status, and port scheduling status; The traffic fluctuation monitoring results include data traffic change trends, risk node traffic fluctuations, nodes with excessive traffic fluctuations, and node path adjustment suggestions; The adjusted order urging task report includes task priority adjustment results, task progress evaluation, node processing capacity evaluation, node delay situation, and task scheduling sequence optimization.

3. The intelligent shipping document reminder system according to claim 1 is characterized by: The data storage management module includes: The data classification submodule obtains the data types and storage requirements of shipping documents, analyzes and compares the access frequency of each type of data, divides each type of data into high-frequency data and low-frequency data according to the access frequency, and generates data classification results; The data storage allocation submodule evaluates the load and storage capacity of each storage node based on the data classification results, and adopts the formula based on the data storage requirements: ； The storage allocation result is obtained by calculation, and the preliminary storage allocation result is generated by combining the node load, storage capacity and data access frequency differences; in, Represents the storage capacity of the SSD node, Represents the storage capacity of the HDD node, and Represent the access frequency of high-frequency and low-frequency data respectively, and Represent the load status of SSD nodes and HDD nodes respectively. Represents storage requirements, Represents the storage allocation result; The storage allocation adjustment submodule monitors the real-time changes of the storage node load according to the preliminary storage allocation result, analyzes the difference between the current storage capacity and the demand, performs dynamic adjustment of storage allocation, and generates data storage allocation results.

4. The intelligent shipping document reminder system according to claim 1 is characterized by: The order urging path optimization module includes: The data access analysis submodule calls the data access frequency and storage capacity of each storage node according to the data storage allocation result, analyzes the node task relevance and data processing requirements in combination with the order urging task priority and progress, calculates and obtains the data access matching degree of each node, and generates the data access matching degree; The node processing capacity evaluation submodule evaluates the processing capacity of each storage node based on the priority and progress of the order urging task, analyzes the storage capacity and load status of the node, and calculates the matching degree between the node processing capacity and the task requirements. The formula is: ； The calculation obtains the matching degree between the node processing capacity and the task requirements, and generates the node processing capacity evaluation result by combining the storage capacity, load status and task requirements; in, Represents the degree of match between the node processing capability and the task requirements. Represents the storage capacity of the node, Represents the load rate of the node, represents the processing time requirement of the task, Represents the priority of the task; The order reminder path optimization submodule optimizes the nodes in the order reminder path according to the data access matching degree and node processing capacity evaluation results, analyzes the task correlation and processing capacity matching between nodes, adjusts the storage resources and task allocation of the nodes in the path, and generates an optimized order reminder path.

5. The intelligent shipping document reminder system according to claim 1 is characterized by: The data flow monitoring module includes: The data collection submodule monitors the data flow information of each node in the execution result of the order urging path in different time periods, collects flow information in real time based on task processing capability and delay risk assessment, and generates real-time data flow information; The data analysis submodule analyzes the data flow change trend of each node according to the real-time data flow information, using the formula: ； Calculate and obtain the traffic fluctuation percentage, compare it with the preset fluctuation threshold, filter out the risk nodes whose fluctuation exceeds the threshold, and generate a traffic fluctuation trend analysis report; in, Represents the flow fluctuation percentage, Represents the node data traffic at the current moment, Represents the node data traffic at the previous moment, represents the average processing delay of the node, Represents the total amount of tasks, Represents the length of the time period; The path adjustment submodule replans the execution path of the risk node according to the traffic fluctuation trend analysis report, analyzes the traffic fluctuation of each risk node, optimizes the execution order between nodes, and generates traffic fluctuation monitoring results.

6. The intelligent shipping document reminder system according to claim 1 is characterized by: The feedback adjustment module comprises: The flow monitoring submodule detects the flow fluctuation amplitude and trend of the current path node according to the flow fluctuation monitoring result, obtains the flow difference and fluctuation rate of each path node, and evaluates the flow fluctuation amplitude of the path node to generate a path flow evaluation result; The path load analysis submodule analyzes the load of the path nodes based on the path flow evaluation results, calculates the load index of each node, compares the relationship between the load and the flow fluctuation amplitude, and selects the path nodes with heavier loads to obtain the load ratio of the path nodes using the formula: ； Obtain the load index of each node through calculation and generate path node load data; in, For the The load index of the nodes, For the The traffic fluctuation amplitude of each node, is the node traffic, is the average flow rate, is the total number of nodes; The task scheduling optimization submodule analyzes the task execution priority of the current path node according to the path node load data and path traffic evaluation results, calculates the adjustment requirements of the task scheduling sequence, adjusts the node task scheduling sequence, determines whether the task needs to be re-planned, and generates an adjusted order urging task report.

7. An intelligent method for urging shipping documents, characterized in that: The intelligent shipping document reminder system according to any one of claims 1 to 6 comprises the following steps: S1: Obtain the data type, storage requirements and access frequency of shipping documents, divide the data into high-frequency data and low-frequency data according to the access frequency, store the high-frequency data in SSD nodes and the low-frequency data in HDD nodes, monitor the node load and storage capacity in real time, dynamically adjust the data storage allocation according to the current load situation, and generate the storage allocation result; S2: calling the data access frequency and storage capacity in the storage allocation result, analyzing the task relevance and processing capacity of each node, combining the priority and progress of the order urging task, evaluating the matching degree of the storage and processing capacity of the nodes in the path, and generating an optimized order urging path; S3: According to the task execution status and progress information of each node in the optimized order urging path, combined with the ship's navigation status and port scheduling status, the task's processing capacity and current progress are evaluated in real time, the delay risk is determined, and the task execution path is adjusted based on the evaluation result to generate the order urging path execution result; S4: Based on the execution result of the order urging path, monitor the real-time data flow of the path nodes, analyze the data flow change trend, screen the risk nodes whose flow fluctuation exceeds the fluctuation threshold, readjust the execution path of the risk nodes, and generate the flow fluctuation monitoring result; S5: Based on the traffic fluctuation monitoring results, analyze the traffic fluctuation amplitude and path load of the current path node, dynamically adjust the path node task execution priority, evaluate the task progress, node processing capacity and delay of each node, determine whether the task scheduling order needs to be adjusted, and generate an adjusted order urging task report.

Images